Bibliographic record
Abstract
Abstract Speech production is an important aspect of linguistic competence. An attempt to understand linguistic morphology without speech production would be incomplete. A central research question develops from this perspective: what is the role of morphology in speech production. Speech production researchers collect many different types of data and much of that data has informed how linguists and psycholinguists characterize the role of linguistic morphology in speech production. Models of speech production play an important role in the investigation of linguistic morphology. These models provide a framework, which allows researchers to explore the role of morphology in speech production. However, models of speech production generally focus on different aspects of the production process. These models are split between phonetic models (which attempt to understand how the brain creates motor commands for uttering and articulating speech) and psycholinguistic models (which attempt to understand the cognitive processes and representation of the production process). Models that merge these two model types, phonetic and psycholinguistic models, have the potential to allow researchers the possibility to make specific predictions about the effects of morphology on speech production. Many studies have explored models of speech production, but the investigation of the role of morphology and how morphological properties may be represented in merged speech production models is limited.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.012 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".